A Nonparametric Approach to Identifying a Subset of Forecasters that Outperforms the Simple Average
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- Constantin Bürgi & Tara M. Sinclair, 2017. "A nonparametric approach to identifying a subset of forecasters that outperforms the simple average," Empirical Economics, Springer, vol. 53(1), pages 101-115, August.
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Cited by:
- Constantin Rudolf Salomo Bürgi, 2023.
"How to deal with missing observations in surveys of professional forecasters,"
Journal of Applied Economics, Taylor & Francis Journals, vol. 26(1), pages 2185975-218, December.
- Constantin Bürgi, 2023. "How to Deal With Missing Observations in Surveys of Professional Forecasters," CESifo Working Paper Series 10203, CESifo.
- Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024.
"Flexible global forecast combinations,"
Omega, Elsevier, vol. 126(C).
- Ryan Thompson & Yilin Qian & Andrey L. Vasnev, 2022. "Flexible global forecast combinations," Papers 2207.07318, arXiv.org, revised Mar 2024.
- Fernando Faure & Carlos A. Medel, 2020. "Does the Exposure to the Business Cycle Improve Consumer Perceptions for Forecasting? Microdata Evidence from Chile," Working Papers Central Bank of Chile 888, Central Bank of Chile.
- Tim Köhler & Jörg Döpke, 2023. "Will the last be the first? Ranking German macroeconomic forecasters based on different criteria," Empirical Economics, Springer, vol. 64(2), pages 797-832, February.
- Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
- Diebold, Francis X. & Shin, Minchul, 2019.
"Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives,"
International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
- Francis X. Diebold & Minchul Shin, 2018. "Machine Learning for Regularized Survey Forecast Combination: Partially Egalitarian Lasso and its Derivatives," PIER Working Paper Archive 18-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 17 Aug 2018.
- Francis X. Diebold & Minchul Shin, 2018. "Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives," NBER Working Papers 24967, National Bureau of Economic Research, Inc.
- Yongchen Zhao, 2021.
"The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms,"
Empirical Economics, Springer, vol. 61(1), pages 173-199, July.
- Yongchen Zhao, 2015. "Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms," Working Papers 2015-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
- Yongchen Zhao, 2015. "Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms," Working Papers 2015-04, Towson University, Department of Economics, revised Mar 2020.
- Constantin Bürgi & Tara M. Sinclair, 2021.
"What does forecaster disagreement tell us about the state of the economy?,"
Applied Economics Letters, Taylor & Francis Journals, vol. 28(1), pages 49-53, January.
- Constantin Bürgi & Tara M. Sinclair, 2020. "What Does Forecaster Disagreement Tell Us about the State of the Economy?," Working Papers 2020-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
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More about this item
Keywords
Forecast combination; Forecast evaluation; Multiple model comparisons; Real-time data; Survey of Professional Forecasters;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2016-05-28 (Econometrics)
- NEP-FOR-2016-05-28 (Forecasting)
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